Ante Odic, Marko Tkalcic, A. Kosir,
"Managing Irrelevant Contextual Categories in a Movie Recommender System."
: Proceedings of the 3rd Workshop on Human Decision Making in Recommender Systems in conjunction with the 7th ACM Conference on Recommender Systems (RecSys 2013), 2013
Original Titel:
Managing Irrelevant Contextual Categories in a Movie Recommender System.
Sprache des Titels:
Englisch
Original Buchtitel:
Proceedings of the 3rd Workshop on Human Decision Making in Recommender Systems in conjunction with the 7th ACM Conference on Recommender Systems (RecSys 2013)
Original Kurzfassung:
Since the users' decision making depends on the situation the
user is in, contextual information has shown to improve the
recommendation procedure in context-aware recommender
systems (RS). In our previous work we have shown that
relevant contextual factors have significantly improved the
quality of rating prediction in RS, while the irrelevant ones
have degraded the prediction. In this work we focus on the
detection of relevant contextual conditions (i.e., values of
contextual factors) which influence the users' decision mak-
ing process. The goals are (i) to lower the intrusion for the
end user by simplifying the acquisition process, and (ii) to
reduce the sparsity of the acquired data during the contextual
modeling. The results showed significant improvement
in the rating prediction task, when managing the irrelevant
contextual conditions by the approach that we propose in
this paper.
Forschungs-